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TELKOMNIKA (Telecommunication Computing Electronics and Control)
Published by Universitas Ahmad Dahlan
ISSN : 16936930     EISSN : 23029293     DOI : 10.12928
Core Subject : Science,
Computer Science & IT
Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of submissions that TELKOMNIKA has received during the last few months the duration of the review process can be up to 14 weeks. Communication Engineering, Computer Network and System Engineering, Computer Science and Information System, Machine Learning, AI and Soft Computing, Signal, Image and Video Processing, Electronics Engineering, Electrical Power Engineering, Power Electronics and Drives, Instrumentation and Control Engineering, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 3,452 Documents
 299   300   301   302   303 
Website-based: smart goat farm monitoring cages Dwinanda Hafid Wicaksana; Wella Wella
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26426

Abstract

Goat farming holds significant profit potential due to high community demand for goat meat and its role in supporting national food security. To optimize its development, proper and efficient farm management is essential. This study aims to design a system for monitoring and improving goat farming by observing key environmental and animal health conditions, such as feed availability, temperature, humidity, and overall livestock health. The proposed system utilizes internet of things (IoT) technology and cloud storage to create a smart farm environment. Various IoT devices, including cameras, thermal sensors, and lighting equipment, are integrated and connected via Wi-Fi. These devices collect real-time data, which is then processed into informative analytics to monitor and support farm development. Through the use of IoT and cloud-based solutions, this system is expected to enable real-time supervision and create ideal, controlled conditions for goat farming, ultimately benefiting farmers.
Plant species identification based on leaf venation features using SVM Agus Ambarwari; Qadhli Jafar Adrian; Yeni Herdiyeni; Irman Hermadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14062

Abstract

The purpose of this study is to identify plant species using leaf venation features. Leaf venation features were obtained through the extraction of leaf venation features. The leaf image segmentation was performed to obtain the binary image of the leaf venation which is then determined the branching point and ending point. From these points, the extraction of leaf venation feature was performed by calculating the value of straightness, a different angle, length ratio, scale projection, skeleton length, number of segments, total skeleton length, number of branching points and number of ending points. So that from the extraction of leaf venation features 19 features were obtained. Identification of plant species was carried out using Support Vector Machine (SVM) with RBF kernel. The learning model was built using 75% of the training data. The testing results using 25% of the data on the training model, obtained an accuracy of 82.67%, with an average of precision of 84% and recall of 83%. 
Design and simulation of high efficiency rectangular microstrip patch antenna using artificial intelligence for 6G era Saad A. Ayoob; Firas S. Alsharbaty; Amina N. Hammodat
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 6: December 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i6.25389

Abstract

Sixth-generation (6G) applications require ultra-speed and large-capacity wireless communication services. Millimeter wave technology can be used to satisfy these requirements, especially at 28 GHz. This paper study used the Ansys® high-frequency structure simulator (HFSS) to design and simulate rectangular and slotted rectangular microstrip patch antennas (MSPAs) at 28 GHz. The proposed designs contained a Rogers RT/Duroid® 5,880 substrate with a dielectric constant (εr) of 2.2 and a loss tangent of 0.0009. The performance of both the proposed antennas was compared to determine which was more efficient. This present study also used an adaptive network-based fuzzy inference system (ANFIS) to determine the optimal frequency and gain. The main objective of the manuscript is to use artificial intelligence (AI) to obtain the best design results for MSPA. The results indicated, with the use of AI, the gain of the rectangular and slotted antennas, was 6.3943 and 6.3094 dB at an efficiency of 98.338% and 98.651%, respectively.
Brain tumor segmentation using multi-level Otsu thresholding and Chan-Vese active contour model Heru Pramono Hadi; Edi Faisal; Eko Hari Rachmawanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 4: August 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i4.21679

Abstract

Research on brain tumor segmentation has been developed, ranging from threshold-based methods to the use of the deep learning algorithm. In this study, we proposed a region-based brain tumor segmentation method, namely the active contour model (ACM). Tumor segmentation was carried out using fluid attenuated inversion recovery (FLAIR) modality magnetic resonance imaging (MRI) image data obtained from the multimodal brain tumor image segmentation benchmark (BRATS) 2015 dataset of 86 images. The initial stage of our segmentation method is to find the initial initialization point/area for the ACM algorithm using multi-level Otsu thresholding, with the level used in this study is 3 levels. After the initial initialization area has been obtained, the segmentation process is continued with ACM which explores the tumor area to obtain a full and accurate tumor area result. The results of this study obtained dice similarity (DS) for our study of 0.7856 with a total time required of 28.080722 seconds, which better than other method that we also compared with ours, 0.75 compared to 0.78 in term of DS.
Efficient IoT-based smart irrigation system using LoRaWAN for resource optimization in agriculture Faten Ben Aicha; Imen Ayachi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27326

Abstract

The global agricultural sector faces critical challenges such as climate change, water scarcity, and inefficient irrigation practices. This paper presents an internet of things (IoT)-based smart irrigation system designed to optimize water usage and enhance agricultural productivity in Tunisia’s semi-arid regions. The proposed system integrates sensors (YL-69 soil moisture, DHT22 temperature-humidity, FC-37 rain), a STM32L072Z LRWAN1 board, and long-range wide area network (LoRaWAN) communication to transmit real-time data to a ThingPark server and MongoDB database. A mobile application developed in Flutter enables monitoring and control through manual, automated, and event-driven modes. Experimental validation demonstrates water savings and improved irrigation efficiency compared to conventional systems. Quantitative results, benchmarking, and cost-benefit analysis confirm the system’s affordability, energy sustainability, and scalability. This solution contributes to sustainable agriculture in resource-constrained environments.
The causal loop diagram model of traceability system rental equipment in oil and gas supporting companies Asep Endih Nurhidayat; Rina Fitriana; Didien Suhardini; Asri Nugrahanti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 5: October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i5.26084

Abstract

Traceability in equipment rental systems enhances security, reliability, and operational transparency by providing the ability to accurately track leased equipment. Challenges in implementing traceability include difficulties in collecting accurate data, the absence of standardized recording practices, and the complexities of integrating technology to ensure complete tracking. This research aims to identify variables affecting the traceability system thinking to improve its efficiency in ongoing business processes. A qualitative descriptive approach is used to offer comprehensive insights into implementing traceability in equipment rental systems, focusing on oil and gas support companies. The study employs the causal loop diagram (CLD) method to dynamically map and identify traceability process variables. Findings show that traceability enables more precise tracking of equipment movement and usage, enhancing inventory management and streamlining maintenance. The CLD method reveals the dynamic relationships between system variables such as equipment availability, maintenance needs, and customer satisfaction, which guide continuous improvement. These results provide stakeholders with valuable insights for optimizing efficiency and service quality in equipment rental operations, particularly in oil and gas support companies. Enhanced traceability can significantly boost operational effectiveness and customer satisfaction.
Implementation and analysis of 5G network identification operations at low signal-to-noise ratio Ilya Pyatin; Juliy Boiko; Oleksander Eromenko; Igor Parkhomey
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 3: June 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i3.22893

Abstract

The article investigates the operations of identifying a cellular network and searching for a cell at various signal-to-noise ratios. The estimation of frequency and time displacement, criteria for detecting the primary synchronization signal are presented. The main contribution of this article is the consideration of the step-by-step execution of the 5G cell search procedure in a complex interference environment. The decoding steps of the primary and secondary synchronization signals are being investigated. This is achieved by analyzing the signals at each step of the correlation algorithm for different signal-to-noise ratios. In order to verify the adequacy of the proposed models, a sequence operation for synchronizing 5G mobile networks with base station signals is considered. The dependence the magnitude of the error vector modulus on the signal-to-noise ratio of a physical broadcasting channel is investigated for three different channel profiles without line of sight. As a result of the experiment, the error vector of the physical broadcast channel changes from 55% to 10%, when the signal-to-noise ratio changes from 0 to 20 dB. In the multiple-input multiple-output (MIMO) mode, we received a 3 dB increase in communication energy efficiency. The findings will be useful for 5G system designers to troubleshoot synchronization problems.
Knowing group motivation using Bolzano method on PageRank computation M. Zainal Arifin; Ahmad Naim Che Pee; Sarni Suhaila Rahim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 5: October 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i5.19650

Abstract

On the e-learning system, student assignments are collected, but problems arise that based on observations from two classes of database courses, 73% of students make plagiarism so lecturers need to give motivation to students. Self motivate is needed by with a group of students. For this reason, using a bolzano method or bisection method will provide an overview of the development of the plagiarism trend between groups of students based on scores on similarity score that compute by PageRank algorithm used by Google. Research method is carried out by conducting preliminary observations of plagiarism scores and creating markov matrix. PageRank compute this ranking and Bolzano method find the intersection of two eigenvalues. Bolzano results the maximum and minimum values on range of intervals where each group consists of 5 students. Experiments were conducted in 3 classes of database courses. Trend analysis found that the plagiarism score averaged 54% with a gradient of 3°, this is relatively small but when spread in different groups it becomes larger and student group have a higher plagiarism score. Results implies a way of looking student motivation based on the plagiarism score by a small groupto motivate each other.
Neural network approaches for quality-of-service optimization in software-defined networking environments Muqamuddin Muhib; Rangu Sridevi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27766

Abstract

Software-defined networking (SDN) enables centralized and programmable control of network behavior; however, conventional routing strategies remain largely reactive and struggle to adapt to rapidly changing traffic dynamics. To address this limitation, this study proposes a learning-based SDN routing framework that integrates a long short-term memory (LSTM) model to predict traffic patterns and proactively optimize routing decisions. The proposed approach is implemented and evaluated in an SDN testbed using realistic traffic scenarios. Experimental results are averaged over multiple independent runs to ensure robustness and reproducibility. Compared with static shortest-path routing and classical machine learning (ML) baselines, the proposed model demonstrates consistent improvements in latency, packet loss, and throughput under the evaluated conditions. In particular, the ablation study reports a 95% confidence interval for end-to-end latency ranging from 51.8 to 55.6 ms, confirming the statistical stability of the observed gains. Additional analyses show that the framework maintains low inference latency and modest control overhead, making it suitable for real-time SDN environments. Overall, the findings indicate that temporal learning models can effectively enhance SDN routing performance when evaluated within controlled experimental settings, offering a practical pathway toward more adaptive and intelligent network control.
A comprehensive analysis of eye diseases and medical data classification Raed Alazaidah; Hamza Abu Owida; Nawaf Alshdaifat; Abedalhakeem Issa; Suhaila Abuowaida; Nidal Yousef
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26058

Abstract

Vision loss is a critical health issue that presents substantial challenges to both individuals and communities. For those affected, it can lead to difficulties in performing daily activities, hinder educational and employment opportunities, and significantly impact mental health and overall quality of life. The inability to see can also lead to increased dependence on others, creating emotional and financial strains on families and caregivers. This paper highlights the benefit of machine learning (ML) in exploring conditions that significantly affect vision loss. The goals that will be achieved in this paper are to determine the best classifier capable of dealing with medical datasets and to determine the best strategy for dealing with medical data. Determine which feature selection is most applicable to use for examining medical data. Two medical datasets, 4 strategies, 19 classifiers, and 2 feature selections were used. As for the best classifier, the stochastic gradient descent (SGD) model was the best in dataset 1 and 2. The function strategy showed the best performance, followed by the rules strategy. CorrelationAttributeEval was shown to be the best feature selection, while ClassifierAttributeEval was the second-best feature selection.

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